Make agentic quality fit your context

Connect classical quality engineering with auditable agentic systems. Use PACTS to make quality work observable, contextual, and accountable.

Discuss Your Context Explore Services

Open-source portfolio

Agentic QEQE fleet and skills SentinelAgentic API testing Nagual QELearning knowledge system LionAGI QEMulti-agent orchestration

The PACTS Framework

Five principles for applying agentic systems without abandoning context-driven quality engineering. PACTS makes boundaries, evidence, and human judgment explicit.

P

Proactive

Test before bugs; design testability into the architecture. Shift from reactive firefighting to proactive quality design.

A

Autonomous

Teams and agents own quality and decide within boundaries (guardrails), executing, learning, and adapting without constant human intervention.

C

Collaborative

Whole-team quality. Humans and agents work together — quality becomes a true team sport.

T

Targeted

Risk-focused; test what matters, skip what doesn't. Value-driven quality that delivers business impact.

S

Structured

Governance, observability, and explainability of agent behavior — especially where decisions must be reconstructed and challenged. Structure prevents teams from bolting agentic AI onto existing chaos.

Ways to work together

Start with the smallest engagement that can produce useful evidence.

🔍

Readiness & Risk Workshop

Map the product, quality risks, delivery constraints, and places where agentic assistance could earn its way in.

  • Context and stakeholder map
  • Risk-weighted opportunity assessment
  • PACTS readiness review
  • Decision record and next-step options
🚀

Evidence-Led Pilot

Test one valuable use case against an agreed baseline before scaling tools, agents, or organizational change.

  • Explicit success and stop criteria
  • Human checkpoints and guardrails
  • Implementation with your team
  • Results, limitations, and scale decision
🎓

Team Lab

A hands-on learning format built around your systems, risks, and existing engineering practices.

  • PACTS applied to a real workflow
  • Agent and harness design exercises
  • Verification and failure-mode practice
  • Team learning backlog

Why Quantum QE?

A practical bridge between established QE judgment and an open-source portfolio—not a single framework statistic.

The Bridge Builder Advantage

  • ✓
    Building software systems since 1996 Practical experience across hardware, software delivery, QA/QE leadership, and agentic systems
  • ✓
    Context-Driven Approach No cookie-cutter solutions - every transformation fits your unique context
  • ✓
    Evidence before promise Establish a baseline, test the intervention, and publish the result with its context
  • ✓
    Human-in-the-Loop Philosophy Augment expertise, don't replace it - maintain critical human judgment
  • ✓
    Open-source practice Building Agentic QE in public so capabilities, limits, and implementation choices can be inspected
  • ✓
    Community Builder Growing the Agentics community in Serbia and sharing lessons internationally
Agentic QE
TypeScript · Open source
60 QE agents, 76 user-facing skills, and support for 11 coding-agent platforms.
Sentinel API Testing
Python · Open source
Python platform for API testing with specialized ephemeral agents.
Nagual QE
Rust · Open source
Rust-native self-learning knowledge system for quality engineering.
LionAGI QE Fleet
Python · Open source
Python experimentation with multi-agent QE orchestration.

Bring the difficult quality question.

We will start with context, identify the decision that matters, and agree what evidence would justify the next step.

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